Overview

Dataset statistics

Number of variables60
Number of observations99789
Missing cells0
Missing cells (%)0.0%
Duplicate rows58
Duplicate rows (%)0.1%
Total size in memory45.7 MiB
Average record size in memory480.0 B

Variable types

Categorical37
Numeric23

Warnings

Dataset has 58 (0.1%) duplicate rows Duplicates
topWriters is highly correlated with lowWritersHigh correlation
lowWriters is highly correlated with topWritersHigh correlation
tag_overview is highly skewed (γ1 = 84.08082887) Skewed
num_of_cast has 16971 (17.0%) zeros Zeros
top_10_cast_popularity_mean has 16971 (17.0%) zeros Zeros
num_of_crew has 18740 (18.8%) zeros Zeros
top_10_crew_popularity_mean has 18740 (18.8%) zeros Zeros
cast_classA has 32603 (32.7%) zeros Zeros
cast_classB has 31507 (31.6%) zeros Zeros
crew_classA has 58477 (58.6%) zeros Zeros
crew_classB has 26396 (26.5%) zeros Zeros
popularCasts has 61284 (61.4%) zeros Zeros
notPopularCasts has 84216 (84.4%) zeros Zeros
popularCrews has 78899 (79.1%) zeros Zeros
notPopularCrews has 76896 (77.1%) zeros Zeros
topDirectors has 88160 (88.3%) zeros Zeros
lowDirectors has 84717 (84.9%) zeros Zeros
topWriters has 38935 (39.0%) zeros Zeros
lowWriters has 38935 (39.0%) zeros Zeros
len_overview has 7124 (7.1%) zeros Zeros
tagline_len has 73704 (73.9%) zeros Zeros
tag_overview has 73704 (73.9%) zeros Zeros

Reproduction

Analysis started2021-04-08 02:01:23.974226
Analysis finished2021-04-08 02:04:16.764832
Duration2 minutes and 52.79 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

isAdult
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99654 
1
 
135

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099654
99.9%
1135
 
0.1%
2021-04-07T19:04:17.070562image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:17.154012image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099654
99.9%
1135
 
0.1%

Most occurring characters

ValueCountFrequency (%)
099654
99.9%
1135
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099654
99.9%
1135
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099654
99.9%
1135
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099654
99.9%
1135
 
0.1%

runtimeMinutes
Real number (ℝ≥0)

Distinct1725
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.79054997
Minimum70
Maximum114
Zeros0
Zeros (%)0.0%
Memory size779.7 KiB
2021-04-07T19:04:17.265947image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile75
Q187
median92.15557775
Q396
95-th percentile109
Maximum114
Range44
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.228592336
Coefficient of variation (CV)0.100539678
Kurtosis0.009697800603
Mean91.79054997
Median Absolute Deviation (MAD)5.148477385
Skewness0.0173804493
Sum9159687.191
Variance85.16691651
MonotocityNot monotonic
2021-04-07T19:04:17.415671image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
907196
 
7.2%
87.007100363905
 
3.9%
1003192
 
3.2%
953123
 
3.1%
853035
 
3.0%
802931
 
2.9%
93.823358382774
 
2.8%
922595
 
2.6%
932479
 
2.5%
882247
 
2.3%
Other values (1715)66312
66.5%
ValueCountFrequency (%)
701187
1.2%
71560
 
0.6%
72875
0.9%
73687
0.7%
74706
0.7%
751717
1.7%
76868
0.9%
77836
0.8%
781176
1.2%
79855
0.9%
ValueCountFrequency (%)
114601
 
0.6%
113669
 
0.7%
112793
0.8%
111659
 
0.7%
1101708
1.7%
109813
0.8%
1081095
1.1%
1071123
1.1%
1061127
1.1%
1051939
1.9%

imdb_avgRating
Real number (ℝ≥0)

Distinct92
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.083221598
Minimum0.5
Maximum10
Zeros0
Zeros (%)0.0%
Memory size779.7 KiB
2021-04-07T19:04:17.574332image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile3.5
Q15.2
median6.2
Q37
95-th percentile8.1
Maximum10
Range9.5
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.400148585
Coefficient of variation (CV)0.2301656389
Kurtosis0.2836024101
Mean6.083221598
Median Absolute Deviation (MAD)0.9
Skewness-0.4588472953
Sum607038.6
Variance1.960416061
MonotocityNot monotonic
2021-04-07T19:04:17.743481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.23182
 
3.2%
6.53155
 
3.2%
6.83151
 
3.2%
6.43150
 
3.2%
6.33125
 
3.1%
6.63100
 
3.1%
73035
 
3.0%
6.73009
 
3.0%
6.12896
 
2.9%
62824
 
2.8%
Other values (82)69162
69.3%
ValueCountFrequency (%)
0.518
 
< 0.1%
153
0.1%
1.114
 
< 0.1%
1.220
 
< 0.1%
1.320
 
< 0.1%
1.429
< 0.1%
1.547
< 0.1%
1.645
< 0.1%
1.755
0.1%
1.857
0.1%
ValueCountFrequency (%)
10269
0.3%
9.97
 
< 0.1%
9.813
 
< 0.1%
9.77
 
< 0.1%
9.627
 
< 0.1%
9.539
 
< 0.1%
9.462
 
0.1%
9.386
 
0.1%
9.2119
0.1%
9.1126
0.1%

num_of_cast
Real number (ℝ≥0)

ZEROS

Distinct160
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.558728918
Minimum0
Maximum249
Zeros16971
Zeros (%)17.0%
Memory size779.7 KiB
2021-04-07T19:04:17.896621image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q313
95-th percentile29
Maximum249
Range249
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.58881392
Coefficient of variation (CV)1.212380225
Kurtosis29.05690963
Mean9.558728918
Median Absolute Deviation (MAD)5
Skewness3.867075092
Sum953856
Variance134.300608
MonotocityNot monotonic
2021-04-07T19:04:18.078750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016971
17.0%
56998
 
7.0%
46439
 
6.5%
66378
 
6.4%
75496
 
5.5%
85249
 
5.3%
104445
 
4.5%
94423
 
4.4%
34415
 
4.4%
14034
 
4.0%
Other values (150)34941
35.0%
ValueCountFrequency (%)
016971
17.0%
14034
 
4.0%
23408
 
3.4%
34415
 
4.4%
46439
 
6.5%
56998
7.0%
66378
 
6.4%
75496
 
5.5%
85249
 
5.3%
94423
 
4.4%
ValueCountFrequency (%)
2491
< 0.1%
2261
< 0.1%
2201
< 0.1%
2071
< 0.1%
1991
< 0.1%
1981
< 0.1%
1891
< 0.1%
1881
< 0.1%
1832
< 0.1%
1812
< 0.1%

top_10_cast_popularity_mean
Real number (ℝ≥0)

ZEROS

Distinct43792
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.331386904
Minimum0
Maximum33.139
Zeros16971
Zeros (%)17.0%
Memory size779.7 KiB
2021-04-07T19:04:18.263447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median0.9426666667
Q31.6349
95-th percentile4.11202
Maximum33.139
Range33.139
Interquartile range (IQR)1.0349

Descriptive statistics

Standard deviation1.441740801
Coefficient of variation (CV)1.082886423
Kurtosis22.4300518
Mean1.331386904
Median Absolute Deviation (MAD)0.4042333333
Skewness3.264698348
Sum132857.7677
Variance2.078616537
MonotocityNot monotonic
2021-04-07T19:04:18.419810image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016971
 
17.0%
0.610988
 
11.0%
0.61229
 
1.2%
0.638260
 
0.3%
0.676249
 
0.2%
0.695194
 
0.2%
0.8182
 
0.2%
0.76166
 
0.2%
0.7266666667161
 
0.2%
0.79141
 
0.1%
Other values (43782)69248
69.4%
ValueCountFrequency (%)
016971
17.0%
0.0751
 
< 0.1%
0.61229
 
1.2%
0.610988
11.0%
0.60033333331
 
< 0.1%
0.60053
 
< 0.1%
0.60051
 
< 0.1%
0.60055555561
 
< 0.1%
0.60057142861
 
< 0.1%
0.60061
 
< 0.1%
ValueCountFrequency (%)
33.1391
< 0.1%
29.8841
< 0.1%
29.1421
< 0.1%
25.98491
< 0.1%
25.0741
< 0.1%
23.2711
< 0.1%
23.1471
< 0.1%
22.9421
< 0.1%
21.6561
< 0.1%
21.1042
< 0.1%

num_of_crew
Real number (ℝ≥0)

ZEROS

Distinct344
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.91560192
Minimum0
Maximum964
Zeros18740
Zeros (%)18.8%
Memory size779.7 KiB
2021-04-07T19:04:18.582914image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile28
Maximum964
Range964
Interquartile range (IQR)6

Descriptive statistics

Standard deviation22.47282874
Coefficient of variation (CV)2.83905494
Kurtosis233.04495
Mean7.91560192
Median Absolute Deviation (MAD)2
Skewness11.59988043
Sum789890
Variance505.0280314
MonotocityNot monotonic
2021-04-07T19:04:18.751434image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119372
19.4%
018740
18.8%
212657
12.7%
37838
7.9%
45565
 
5.6%
54409
 
4.4%
63716
 
3.7%
73132
 
3.1%
82729
 
2.7%
92346
 
2.4%
Other values (334)19285
19.3%
ValueCountFrequency (%)
018740
18.8%
119372
19.4%
212657
12.7%
37838
7.9%
45565
 
5.6%
54409
 
4.4%
63716
 
3.7%
73132
 
3.1%
82729
 
2.7%
92346
 
2.4%
ValueCountFrequency (%)
9641
< 0.1%
9101
< 0.1%
8141
< 0.1%
7391
< 0.1%
7102
< 0.1%
6721
< 0.1%
6391
< 0.1%
5761
< 0.1%
5741
< 0.1%
5641
< 0.1%

top_10_crew_popularity_mean
Real number (ℝ≥0)

ZEROS

Distinct14880
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.646568965
Minimum0
Maximum14.987
Zeros18740
Zeros (%)18.8%
Memory size779.7 KiB
2021-04-07T19:04:18.915471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median0.6
Q30.7532
95-th percentile1.4
Maximum14.987
Range14.987
Interquartile range (IQR)0.1532

Descriptive statistics

Standard deviation0.5095543165
Coefficient of variation (CV)0.7880896611
Kurtosis55.36162299
Mean0.646568965
Median Absolute Deviation (MAD)0.094
Skewness4.100035289
Sum64520.47045
Variance0.2596456014
MonotocityNot monotonic
2021-04-07T19:04:19.079859image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.634935
35.0%
018740
18.8%
0.62314
 
2.3%
0.79656
 
0.7%
0.638631
 
0.6%
0.98616
 
0.6%
1.4451
 
0.5%
1437
 
0.4%
0.676424
 
0.4%
0.7266666667393
 
0.4%
Other values (14870)40192
40.3%
ValueCountFrequency (%)
018740
18.8%
0.31
 
< 0.1%
0.35871428571
 
< 0.1%
0.42857142861
 
< 0.1%
0.451
 
< 0.1%
0.5941
 
< 0.1%
0.62314
 
2.3%
0.634935
35.0%
0.6001251
 
< 0.1%
0.60033333331
 
< 0.1%
ValueCountFrequency (%)
14.9871
< 0.1%
14.8821
< 0.1%
14.6271
< 0.1%
13.741
< 0.1%
13.3821
< 0.1%
11.49551
< 0.1%
10.9371
< 0.1%
10.4211
< 0.1%
9.9191
< 0.1%
9.64431
< 0.1%

release_year_int
Real number (ℝ≥0)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.451773
Minimum2000
Maximum2021
Zeros0
Zeros (%)0.0%
Memory size779.7 KiB
2021-04-07T19:04:19.229927image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12007
median2012
Q32016
95-th percentile2019
Maximum2021
Range21
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.463599137
Coefficient of variation (CV)0.002716246648
Kurtosis-0.8399534902
Mean2011.451773
Median Absolute Deviation (MAD)4
Skewness-0.3552404307
Sum200720761
Variance29.85091553
MonotocityNot monotonic
2021-04-07T19:04:19.359311image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20167471
 
7.5%
20157253
 
7.3%
20177145
 
7.2%
20146958
 
7.0%
20136214
 
6.2%
20115747
 
5.8%
20125550
 
5.6%
20105326
 
5.3%
20095071
 
5.1%
20084628
 
4.6%
Other values (12)38426
38.5%
ValueCountFrequency (%)
20002500
2.5%
20012626
2.6%
20022717
2.7%
20032823
2.8%
20043158
3.2%
20053455
3.5%
20063918
3.9%
20074132
4.1%
20084628
4.6%
20095071
5.1%
ValueCountFrequency (%)
2021621
 
0.6%
20204015
4.0%
20194524
4.5%
20183937
3.9%
20177145
7.2%
20167471
7.5%
20157253
7.3%
20146958
7.0%
20136214
6.2%
20125550
5.6%

cast_classA
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.243503793
Minimum0
Maximum10
Zeros32603
Zeros (%)32.7%
Memory size779.7 KiB
2021-04-07T19:04:19.478666image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.44719499
Coefficient of variation (CV)1.062799741
Kurtosis-0.7025185785
Mean3.243503793
Median Absolute Deviation (MAD)2
Skewness0.8039764044
Sum323666
Variance11.8831533
MonotocityNot monotonic
2021-04-07T19:04:19.593711image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
032603
32.7%
111852
 
11.9%
1011065
 
11.1%
29546
 
9.6%
38054
 
8.1%
46902
 
6.9%
55714
 
5.7%
64676
 
4.7%
73810
 
3.8%
83097
 
3.1%
ValueCountFrequency (%)
032603
32.7%
111852
 
11.9%
29546
 
9.6%
38054
 
8.1%
46902
 
6.9%
55714
 
5.7%
64676
 
4.7%
73810
 
3.8%
83097
 
3.1%
92470
 
2.5%
ValueCountFrequency (%)
1011065
11.1%
92470
 
2.5%
83097
 
3.1%
73810
 
3.8%
64676
 
4.7%
55714
5.7%
46902
6.9%
38054
8.1%
29546
9.6%
111852
11.9%

cast_classB
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.800769624
Minimum0
Maximum10
Zeros31507
Zeros (%)31.6%
Memory size779.7 KiB
2021-04-07T19:04:19.713058image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.76275634
Coefficient of variation (CV)0.9864275577
Kurtosis-0.3787633079
Mean2.800769624
Median Absolute Deviation (MAD)2
Skewness0.7632961358
Sum279486
Variance7.632822593
MonotocityNot monotonic
2021-04-07T19:04:19.827784image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
031507
31.6%
310809
 
10.8%
210719
 
10.7%
110511
 
10.5%
410015
 
10.0%
58098
 
8.1%
66068
 
6.1%
74450
 
4.5%
83313
 
3.3%
92492
 
2.5%
ValueCountFrequency (%)
031507
31.6%
110511
 
10.5%
210719
 
10.7%
310809
 
10.8%
410015
 
10.0%
58098
 
8.1%
66068
 
6.1%
74450
 
4.5%
83313
 
3.3%
92492
 
2.5%
ValueCountFrequency (%)
101807
 
1.8%
92492
 
2.5%
83313
 
3.3%
74450
4.5%
66068
6.1%
58098
8.1%
410015
10.0%
310809
10.8%
210719
10.7%
110511
10.5%

cast_classC
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99787 
1
 
1
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099787
> 99.9%
11
 
< 0.1%
71
 
< 0.1%
2021-04-07T19:04:20.085079image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:20.171896image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099787
> 99.9%
11
 
< 0.1%
71
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
099787
> 99.9%
11
 
< 0.1%
71
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099787
> 99.9%
11
 
< 0.1%
71
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099787
> 99.9%
11
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099787
> 99.9%
11
 
< 0.1%
71
 
< 0.1%

crew_classA
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.087664973
Minimum0
Maximum10
Zeros58477
Zeros (%)58.6%
Memory size779.7 KiB
2021-04-07T19:04:20.258161image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.088261488
Coefficient of variation (CV)1.919949193
Kurtosis8.392920142
Mean1.087664973
Median Absolute Deviation (MAD)0
Skewness2.856018362
Sum108537
Variance4.360836041
MonotocityNot monotonic
2021-04-07T19:04:20.380335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
058477
58.6%
120327
 
20.4%
28406
 
8.4%
33948
 
4.0%
102721
 
2.7%
42234
 
2.2%
51341
 
1.3%
6891
 
0.9%
7591
 
0.6%
8457
 
0.5%
ValueCountFrequency (%)
058477
58.6%
120327
 
20.4%
28406
 
8.4%
33948
 
4.0%
42234
 
2.2%
51341
 
1.3%
6891
 
0.9%
7591
 
0.6%
8457
 
0.5%
9396
 
0.4%
ValueCountFrequency (%)
102721
 
2.7%
9396
 
0.4%
8457
 
0.5%
7591
 
0.6%
6891
 
0.9%
51341
 
1.3%
42234
 
2.2%
33948
 
4.0%
28406
8.4%
120327
20.4%

crew_classB
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.845754542
Minimum0
Maximum10
Zeros26396
Zeros (%)26.5%
Memory size779.7 KiB
2021-04-07T19:04:20.496846image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.957838063
Coefficient of variation (CV)1.039386222
Kurtosis-0.3673126552
Mean2.845754542
Median Absolute Deviation (MAD)2
Skewness0.9198253674
Sum283975
Variance8.748806008
MonotocityNot monotonic
2021-04-07T19:04:20.611018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
026396
26.5%
120815
20.9%
212071
12.1%
37764
 
7.8%
46111
 
6.1%
55370
 
5.4%
65089
 
5.1%
74860
 
4.9%
84646
 
4.7%
93960
 
4.0%
ValueCountFrequency (%)
026396
26.5%
120815
20.9%
212071
12.1%
37764
 
7.8%
46111
 
6.1%
55370
 
5.4%
65089
 
5.1%
74860
 
4.9%
84646
 
4.7%
93960
 
4.0%
ValueCountFrequency (%)
102707
 
2.7%
93960
 
4.0%
84646
 
4.7%
74860
 
4.9%
65089
 
5.1%
55370
 
5.4%
46111
 
6.1%
37764
 
7.8%
212071
12.1%
120815
20.9%

crew_classC
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99783 
1
 
4
3
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099783
> 99.9%
14
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
2021-04-07T19:04:20.851465image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:20.930982image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099783
> 99.9%
14
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
099783
> 99.9%
14
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099783
> 99.9%
14
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099783
> 99.9%
14
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099783
> 99.9%
14
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%

popularCasts
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.141869344
Minimum0
Maximum10
Zeros61284
Zeros (%)61.4%
Memory size779.7 KiB
2021-04-07T19:04:21.017308image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.160231179
Coefficient of variation (CV)1.891837442
Kurtosis6.231302671
Mean1.141869344
Median Absolute Deviation (MAD)0
Skewness2.521471751
Sum113946
Variance4.666598746
MonotocityNot monotonic
2021-04-07T19:04:21.135450image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
061284
61.4%
115911
 
15.9%
27550
 
7.6%
34348
 
4.4%
42789
 
2.8%
102039
 
2.0%
51863
 
1.9%
61369
 
1.4%
71038
 
1.0%
8832
 
0.8%
ValueCountFrequency (%)
061284
61.4%
115911
 
15.9%
27550
 
7.6%
34348
 
4.4%
42789
 
2.8%
51863
 
1.9%
61369
 
1.4%
71038
 
1.0%
8832
 
0.8%
9766
 
0.8%
ValueCountFrequency (%)
102039
 
2.0%
9766
 
0.8%
8832
 
0.8%
71038
 
1.0%
61369
 
1.4%
51863
 
1.9%
42789
 
2.8%
34348
 
4.4%
27550
7.6%
115911
15.9%

notPopularCasts
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1861928669
Minimum0
Maximum6
Zeros84216
Zeros (%)84.4%
Memory size779.7 KiB
2021-04-07T19:04:21.247364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4706963587
Coefficient of variation (CV)2.528004248
Kurtosis9.969847512
Mean0.1861928669
Median Absolute Deviation (MAD)0
Skewness2.882370111
Sum18580
Variance0.2215550621
MonotocityNot monotonic
2021-04-07T19:04:21.351876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
084216
84.4%
112979
 
13.0%
22243
 
2.2%
3300
 
0.3%
441
 
< 0.1%
59
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
084216
84.4%
112979
 
13.0%
22243
 
2.2%
3300
 
0.3%
441
 
< 0.1%
59
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
59
 
< 0.1%
441
 
< 0.1%
3300
 
0.3%
22243
 
2.2%
112979
 
13.0%
084216
84.4%

popularCrews
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4368016515
Minimum0
Maximum10
Zeros78899
Zeros (%)79.1%
Memory size779.7 KiB
2021-04-07T19:04:21.480957image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.274979651
Coefficient of variation (CV)2.918898422
Kurtosis26.84328218
Mean0.4368016515
Median Absolute Deviation (MAD)0
Skewness4.777087429
Sum43588
Variance1.625573111
MonotocityNot monotonic
2021-04-07T19:04:21.599374image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
078899
79.1%
113057
 
13.1%
23500
 
3.5%
31393
 
1.4%
4739
 
0.7%
10498
 
0.5%
5486
 
0.5%
6354
 
0.4%
7311
 
0.3%
8283
 
0.3%
ValueCountFrequency (%)
078899
79.1%
113057
 
13.1%
23500
 
3.5%
31393
 
1.4%
4739
 
0.7%
5486
 
0.5%
6354
 
0.4%
7311
 
0.3%
8283
 
0.3%
9269
 
0.3%
ValueCountFrequency (%)
10498
 
0.5%
9269
 
0.3%
8283
 
0.3%
7311
 
0.3%
6354
 
0.4%
5486
 
0.5%
4739
 
0.7%
31393
 
1.4%
23500
 
3.5%
113057
13.1%

notPopularCrews
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3187225045
Minimum0
Maximum7
Zeros76896
Zeros (%)77.1%
Memory size779.7 KiB
2021-04-07T19:04:21.733066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6785842009
Coefficient of variation (CV)2.129075266
Kurtosis8.767169737
Mean0.3187225045
Median Absolute Deviation (MAD)0
Skewness2.657385816
Sum31805
Variance0.4604765177
MonotocityNot monotonic
2021-04-07T19:04:21.845950image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
076896
77.1%
116412
 
16.4%
24676
 
4.7%
31323
 
1.3%
4364
 
0.4%
594
 
0.1%
622
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
076896
77.1%
116412
 
16.4%
24676
 
4.7%
31323
 
1.3%
4364
 
0.4%
594
 
0.1%
622
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
72
 
< 0.1%
622
 
< 0.1%
594
 
0.1%
4364
 
0.4%
31323
 
1.3%
24676
 
4.7%
116412
 
16.4%
076896
77.1%

hasMoreTitles
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
1
77102 
0
22687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
177102
77.3%
022687
 
22.7%
2021-04-07T19:04:22.118209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:22.253747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
177102
77.3%
022687
 
22.7%

Most occurring characters

ValueCountFrequency (%)
177102
77.3%
022687
 
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
177102
77.3%
022687
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
177102
77.3%
022687
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
177102
77.3%
022687
 
22.7%

isHoliday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
93157 
1
 
6632

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
093157
93.4%
16632
 
6.6%
2021-04-07T19:04:22.446220image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:22.519663image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
093157
93.4%
16632
 
6.6%

Most occurring characters

ValueCountFrequency (%)
093157
93.4%
16632
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
093157
93.4%
16632
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
093157
93.4%
16632
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
093157
93.4%
16632
 
6.6%

hasHomepage
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
1
77790 
0
21999 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
177790
78.0%
021999
 
22.0%
2021-04-07T19:04:22.731554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:22.806811image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
177790
78.0%
021999
 
22.0%

Most occurring characters

ValueCountFrequency (%)
177790
78.0%
021999
 
22.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
177790
78.0%
021999
 
22.0%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
177790
78.0%
021999
 
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
177790
78.0%
021999
 
22.0%

hasCollection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
1
95596 
0
 
4193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
195596
95.8%
04193
 
4.2%
2021-04-07T19:04:23.008090image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:23.084158image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
195596
95.8%
04193
 
4.2%

Most occurring characters

ValueCountFrequency (%)
195596
95.8%
04193
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
195596
95.8%
04193
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
195596
95.8%
04193
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
195596
95.8%
04193
 
4.2%

isMusic
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
96241 
1
 
3548

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
096241
96.4%
13548
 
3.6%
2021-04-07T19:04:23.277214image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:23.353276image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
096241
96.4%
13548
 
3.6%

Most occurring characters

ValueCountFrequency (%)
096241
96.4%
13548
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
096241
96.4%
13548
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
096241
96.4%
13548
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
096241
96.4%
13548
 
3.6%

isNews
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99324 
1
 
465

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099324
99.5%
1465
 
0.5%
2021-04-07T19:04:23.550448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:23.636814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099324
99.5%
1465
 
0.5%

Most occurring characters

ValueCountFrequency (%)
099324
99.5%
1465
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099324
99.5%
1465
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099324
99.5%
1465
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099324
99.5%
1465
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
96520 
1
 
3269

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
096520
96.7%
13269
 
3.3%
2021-04-07T19:04:23.833963image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:23.907626image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
096520
96.7%
13269
 
3.3%

Most occurring characters

ValueCountFrequency (%)
096520
96.7%
13269
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
096520
96.7%
13269
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
096520
96.7%
13269
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
096520
96.7%
13269
 
3.3%

isComedy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
72557 
1
27232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
072557
72.7%
127232
 
27.3%
2021-04-07T19:04:24.101438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:24.182288image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
072557
72.7%
127232
 
27.3%

Most occurring characters

ValueCountFrequency (%)
072557
72.7%
127232
 
27.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
072557
72.7%
127232
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
072557
72.7%
127232
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
072557
72.7%
127232
 
27.3%

isReality-TV
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99771 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099771
> 99.9%
118
 
< 0.1%
2021-04-07T19:04:24.372269image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:24.452217image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099771
> 99.9%
118
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
099771
> 99.9%
118
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099771
> 99.9%
118
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099771
> 99.9%
118
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099771
> 99.9%
118
 
< 0.1%

isAction
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
88142 
1
11647 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
088142
88.3%
111647
 
11.7%
2021-04-07T19:04:24.665232image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:24.745966image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
088142
88.3%
111647
 
11.7%

Most occurring characters

ValueCountFrequency (%)
088142
88.3%
111647
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
088142
88.3%
111647
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
088142
88.3%
111647
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
088142
88.3%
111647
 
11.7%

isHorror
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
89001 
1
10788 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
089001
89.2%
110788
 
10.8%
2021-04-07T19:04:24.960993image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:25.040323image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
089001
89.2%
110788
 
10.8%

Most occurring characters

ValueCountFrequency (%)
089001
89.2%
110788
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
089001
89.2%
110788
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
089001
89.2%
110788
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
089001
89.2%
110788
 
10.8%

isDrama
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
51497 
1
48292 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
051497
51.6%
148292
48.4%
2021-04-07T19:04:25.246332image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:25.321179image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
051497
51.6%
148292
48.4%

Most occurring characters

ValueCountFrequency (%)
051497
51.6%
148292
48.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
051497
51.6%
148292
48.4%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
051497
51.6%
148292
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
051497
51.6%
148292
48.4%

isSci-Fi
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
97061 
1
 
2728

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
097061
97.3%
12728
 
2.7%
2021-04-07T19:04:25.513957image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:25.591108image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
097061
97.3%
12728
 
2.7%

Most occurring characters

ValueCountFrequency (%)
097061
97.3%
12728
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
097061
97.3%
12728
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
097061
97.3%
12728
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
097061
97.3%
12728
 
2.7%

isThriller
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
85069 
1
14720 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
085069
85.2%
114720
 
14.8%
2021-04-07T19:04:25.794572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:25.869281image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
085069
85.2%
114720
 
14.8%

Most occurring characters

ValueCountFrequency (%)
085069
85.2%
114720
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
085069
85.2%
114720
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
085069
85.2%
114720
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
085069
85.2%
114720
 
14.8%

isDocumentary
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
77903 
1
21886 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
077903
78.1%
121886
 
21.9%
2021-04-07T19:04:26.074849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:26.148010image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
077903
78.1%
121886
 
21.9%

Most occurring characters

ValueCountFrequency (%)
077903
78.1%
121886
 
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
077903
78.1%
121886
 
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
077903
78.1%
121886
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
077903
78.1%
121886
 
21.9%

isBiography
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
95445 
1
 
4344

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
095445
95.6%
14344
 
4.4%
2021-04-07T19:04:27.452791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:27.526929image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
095445
95.6%
14344
 
4.4%

Most occurring characters

ValueCountFrequency (%)
095445
95.6%
14344
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
095445
95.6%
14344
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
095445
95.6%
14344
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
095445
95.6%
14344
 
4.4%

isTalk-Show
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99788 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099788
> 99.9%
11
 
< 0.1%
2021-04-07T19:04:27.733222image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:27.815037image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099788
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
099788
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099788
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099788
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099788
> 99.9%
11
 
< 0.1%

isShort
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99786 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099786
> 99.9%
13
 
< 0.1%
2021-04-07T19:04:28.013569image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:28.096776image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099786
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
099786
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099786
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099786
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099786
> 99.9%
13
 
< 0.1%

num_genres
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.054525048
Minimum0
Maximum9
Zeros569
Zeros (%)0.6%
Memory size779.7 KiB
2021-04-07T19:04:28.168625image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.085800485
Coefficient of variation (CV)0.5284922109
Kurtosis0.3392645597
Mean2.054525048
Median Absolute Deviation (MAD)1
Skewness0.8133907336
Sum205019
Variance1.178962693
MonotocityNot monotonic
2021-04-07T19:04:28.279930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
138529
38.6%
228147
28.2%
322826
22.9%
47378
 
7.4%
51916
 
1.9%
0569
 
0.6%
6357
 
0.4%
754
 
0.1%
811
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
0569
 
0.6%
138529
38.6%
228147
28.2%
322826
22.9%
47378
 
7.4%
51916
 
1.9%
6357
 
0.4%
754
 
0.1%
811
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
811
 
< 0.1%
754
 
0.1%
6357
 
0.4%
51916
 
1.9%
47378
 
7.4%
322826
22.9%
228147
28.2%
138529
38.6%
0569
 
0.6%

topDirectors
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1381414785
Minimum0
Maximum10
Zeros88160
Zeros (%)88.3%
Memory size779.7 KiB
2021-04-07T19:04:28.392546image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4218821214
Coefficient of variation (CV)3.053985855
Kurtosis40.50938138
Mean0.1381414785
Median Absolute Deviation (MAD)0
Skewness4.505065402
Sum13785
Variance0.1779845244
MonotocityNot monotonic
2021-04-07T19:04:28.503897image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
088160
88.3%
19866
 
9.9%
21534
 
1.5%
3161
 
0.2%
431
 
< 0.1%
513
 
< 0.1%
612
 
< 0.1%
95
 
< 0.1%
84
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
088160
88.3%
19866
 
9.9%
21534
 
1.5%
3161
 
0.2%
431
 
< 0.1%
513
 
< 0.1%
612
 
< 0.1%
84
 
< 0.1%
95
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
103
 
< 0.1%
95
 
< 0.1%
84
 
< 0.1%
612
 
< 0.1%
513
 
< 0.1%
431
 
< 0.1%
3161
 
0.2%
21534
 
1.5%
19866
 
9.9%
088160
88.3%

lowDirectors
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1693272806
Minimum0
Maximum7
Zeros84717
Zeros (%)84.9%
Memory size779.7 KiB
2021-04-07T19:04:28.606218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4397143034
Coefficient of variation (CV)2.596830835
Kurtosis23.19591618
Mean0.1693272806
Median Absolute Deviation (MAD)0
Skewness3.601950773
Sum16897
Variance0.1933486686
MonotocityNot monotonic
2021-04-07T19:04:28.728806image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
084717
84.9%
113684
 
13.7%
21170
 
1.2%
3107
 
0.1%
440
 
< 0.1%
537
 
< 0.1%
631
 
< 0.1%
73
 
< 0.1%
ValueCountFrequency (%)
084717
84.9%
113684
 
13.7%
21170
 
1.2%
3107
 
0.1%
440
 
< 0.1%
537
 
< 0.1%
631
 
< 0.1%
73
 
< 0.1%
ValueCountFrequency (%)
73
 
< 0.1%
631
 
< 0.1%
537
 
< 0.1%
440
 
< 0.1%
3107
 
0.1%
21170
 
1.2%
113684
 
13.7%
084717
84.9%

topWriters
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.09499043
Minimum0
Maximum37
Zeros38935
Zeros (%)39.0%
Memory size779.7 KiB
2021-04-07T19:04:28.849992image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum37
Range37
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.270917049
Coefficient of variation (CV)1.160664983
Kurtosis14.44692817
Mean1.09499043
Median Absolute Deviation (MAD)1
Skewness2.162512385
Sum109268
Variance1.615230145
MonotocityNot monotonic
2021-04-07T19:04:28.976737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
038935
39.0%
132599
32.7%
216802
16.8%
36590
 
6.6%
42765
 
2.8%
51300
 
1.3%
6414
 
0.4%
7174
 
0.2%
898
 
0.1%
940
 
< 0.1%
Other values (11)72
 
0.1%
ValueCountFrequency (%)
038935
39.0%
132599
32.7%
216802
16.8%
36590
 
6.6%
42765
 
2.8%
51300
 
1.3%
6414
 
0.4%
7174
 
0.2%
898
 
0.1%
940
 
< 0.1%
ValueCountFrequency (%)
371
 
< 0.1%
211
 
< 0.1%
191
 
< 0.1%
173
 
< 0.1%
164
 
< 0.1%
152
 
< 0.1%
146
< 0.1%
133
 
< 0.1%
1210
< 0.1%
1114
< 0.1%

lowWriters
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.09499043
Minimum0
Maximum37
Zeros38935
Zeros (%)39.0%
Memory size779.7 KiB
2021-04-07T19:04:29.106815image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum37
Range37
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.270917049
Coefficient of variation (CV)1.160664983
Kurtosis14.44692817
Mean1.09499043
Median Absolute Deviation (MAD)1
Skewness2.162512385
Sum109268
Variance1.615230145
MonotocityNot monotonic
2021-04-07T19:04:29.235188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
038935
39.0%
132599
32.7%
216802
16.8%
36590
 
6.6%
42765
 
2.8%
51300
 
1.3%
6414
 
0.4%
7174
 
0.2%
898
 
0.1%
940
 
< 0.1%
Other values (11)72
 
0.1%
ValueCountFrequency (%)
038935
39.0%
132599
32.7%
216802
16.8%
36590
 
6.6%
42765
 
2.8%
51300
 
1.3%
6414
 
0.4%
7174
 
0.2%
898
 
0.1%
940
 
< 0.1%
ValueCountFrequency (%)
371
 
< 0.1%
211
 
< 0.1%
191
 
< 0.1%
173
 
< 0.1%
164
 
< 0.1%
152
 
< 0.1%
146
< 0.1%
133
 
< 0.1%
1210
< 0.1%
1114
< 0.1%

is_en
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
1
52086 
0
47703 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1
ValueCountFrequency (%)
152086
52.2%
047703
47.8%
2021-04-07T19:04:29.473934image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:29.547407image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
152086
52.2%
047703
47.8%

Most occurring characters

ValueCountFrequency (%)
152086
52.2%
047703
47.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
152086
52.2%
047703
47.8%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
152086
52.2%
047703
47.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
152086
52.2%
047703
47.8%

is_es
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
93658 
1
 
6131

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
093658
93.9%
16131
 
6.1%
2021-04-07T19:04:29.739178image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:29.825986image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
093658
93.9%
16131
 
6.1%

Most occurring characters

ValueCountFrequency (%)
093658
93.9%
16131
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
093658
93.9%
16131
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
093658
93.9%
16131
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
093658
93.9%
16131
 
6.1%

is_fr
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
94873 
1
 
4916

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
094873
95.1%
14916
 
4.9%
2021-04-07T19:04:30.016301image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:30.093387image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
094873
95.1%
14916
 
4.9%

Most occurring characters

ValueCountFrequency (%)
094873
95.1%
14916
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
094873
95.1%
14916
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
094873
95.1%
14916
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
094873
95.1%
14916
 
4.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
93679 
1
 
6110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
093679
93.9%
16110
 
6.1%
2021-04-07T19:04:30.296910image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:30.405120image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
093679
93.9%
16110
 
6.1%

Most occurring characters

ValueCountFrequency (%)
093679
93.9%
16110
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
093679
93.9%
16110
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
093679
93.9%
16110
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
093679
93.9%
16110
 
6.1%

is_murder
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
98685 
1
 
1104

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
098685
98.9%
11104
 
1.1%
2021-04-07T19:04:30.727589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:30.844793image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
098685
98.9%
11104
 
1.1%

Most occurring characters

ValueCountFrequency (%)
098685
98.9%
11104
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
098685
98.9%
11104
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
098685
98.9%
11104
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
098685
98.9%
11104
 
1.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
98719 
1
 
1070

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
098719
98.9%
11070
 
1.1%
2021-04-07T19:04:31.108074image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:31.201915image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
098719
98.9%
11070
 
1.1%

Most occurring characters

ValueCountFrequency (%)
098719
98.9%
11070
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
098719
98.9%
11070
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
098719
98.9%
11070
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
098719
98.9%
11070
 
1.1%

len_overview
Real number (ℝ≥0)

ZEROS

Distinct998
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.2908737
Minimum0
Maximum1000
Zeros7124
Zeros (%)7.1%
Memory size779.7 KiB
2021-04-07T19:04:31.316838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1142
median248
Q3436
95-th percentile771
Maximum1000
Range1000
Interquartile range (IQR)294

Descriptive statistics

Standard deviation226.8325021
Coefficient of variation (CV)0.735774301
Kurtosis0.2368174411
Mean308.2908737
Median Absolute Deviation (MAD)133
Skewness0.8894303153
Sum30764038
Variance51452.98402
MonotocityNot monotonic
2021-04-07T19:04:31.533494image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07124
 
7.1%
149329
 
0.3%
150322
 
0.3%
238322
 
0.3%
239305
 
0.3%
146305
 
0.3%
147305
 
0.3%
236302
 
0.3%
148298
 
0.3%
136284
 
0.3%
Other values (988)89893
90.1%
ValueCountFrequency (%)
07124
7.1%
110
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%
42
 
< 0.1%
61
 
< 0.1%
96
 
< 0.1%
102
 
< 0.1%
1141
 
< 0.1%
1215
 
< 0.1%
ValueCountFrequency (%)
100027
< 0.1%
99933
< 0.1%
99818
< 0.1%
99727
< 0.1%
99617
< 0.1%
99527
< 0.1%
99419
< 0.1%
99317
< 0.1%
99219
< 0.1%
99124
< 0.1%

is_Canal+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99159 
1
 
630

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099159
99.4%
1630
 
0.6%
2021-04-07T19:04:31.861576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:31.939424image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099159
99.4%
1630
 
0.6%

Most occurring characters

ValueCountFrequency (%)
099159
99.4%
1630
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099159
99.4%
1630
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099159
99.4%
1630
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099159
99.4%
1630
 
0.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99395 
1
 
394

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099395
99.6%
1394
 
0.4%
2021-04-07T19:04:32.139864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:32.223701image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099395
99.6%
1394
 
0.4%

Most occurring characters

ValueCountFrequency (%)
099395
99.6%
1394
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099395
99.6%
1394
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099395
99.6%
1394
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099395
99.6%
1394
 
0.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99425 
1
 
364

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099425
99.6%
1364
 
0.4%
2021-04-07T19:04:32.423060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:32.498101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099425
99.6%
1364
 
0.4%

Most occurring characters

ValueCountFrequency (%)
099425
99.6%
1364
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099425
99.6%
1364
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099425
99.6%
1364
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099425
99.6%
1364
 
0.4%

is_CNC
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99445 
1
 
344

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099445
99.7%
1344
 
0.3%
2021-04-07T19:04:32.694384image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:32.785538image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099445
99.7%
1344
 
0.3%

Most occurring characters

ValueCountFrequency (%)
099445
99.7%
1344
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099445
99.7%
1344
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099445
99.7%
1344
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099445
99.7%
1344
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99456 
1
 
333

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099456
99.7%
1333
 
0.3%
2021-04-07T19:04:33.009635image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:33.100153image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099456
99.7%
1333
 
0.3%

Most occurring characters

ValueCountFrequency (%)
099456
99.7%
1333
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099456
99.7%
1333
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099456
99.7%
1333
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099456
99.7%
1333
 
0.3%

is_ARTE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99467 
1
 
322

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099467
99.7%
1322
 
0.3%
2021-04-07T19:04:33.304806image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:33.388136image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099467
99.7%
1322
 
0.3%

Most occurring characters

ValueCountFrequency (%)
099467
99.7%
1322
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099467
99.7%
1322
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099467
99.7%
1322
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099467
99.7%
1322
 
0.3%

is_ZDF
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99484 
1
 
305

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099484
99.7%
1305
 
0.3%
2021-04-07T19:04:33.595395image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:33.740187image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099484
99.7%
1305
 
0.3%

Most occurring characters

ValueCountFrequency (%)
099484
99.7%
1305
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099484
99.7%
1305
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099484
99.7%
1305
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099484
99.7%
1305
 
0.3%

is_Rai_Cinema
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99504 
1
 
285

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099504
99.7%
1285
 
0.3%
2021-04-07T19:04:33.957642image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:34.049321image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099504
99.7%
1285
 
0.3%

Most occurring characters

ValueCountFrequency (%)
099504
99.7%
1285
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099504
99.7%
1285
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099504
99.7%
1285
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099504
99.7%
1285
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99511 
1
 
278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099511
99.7%
1278
 
0.3%
2021-04-07T19:04:34.259506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:34.342891image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099511
99.7%
1278
 
0.3%

Most occurring characters

ValueCountFrequency (%)
099511
99.7%
1278
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099511
99.7%
1278
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099511
99.7%
1278
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099511
99.7%
1278
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
99515 
1
 
274

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
099515
99.7%
1274
 
0.3%
2021-04-07T19:04:34.557456image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-07T19:04:34.639427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
099515
99.7%
1274
 
0.3%

Most occurring characters

ValueCountFrequency (%)
099515
99.7%
1274
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99789
100.0%

Most frequent character per category

ValueCountFrequency (%)
099515
99.7%
1274
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common99789
100.0%

Most frequent character per script

ValueCountFrequency (%)
099515
99.7%
1274
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99789
100.0%

Most frequent character per block

ValueCountFrequency (%)
099515
99.7%
1274
 
0.3%

tagline_len
Real number (ℝ≥0)

ZEROS

Distinct231
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.43153053
Minimum0
Maximum271
Zeros73704
Zeros (%)73.9%
Memory size779.7 KiB
2021-04-07T19:04:34.751797image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile53
Maximum271
Range271
Interquartile range (IQR)14

Descriptive statistics

Standard deviation21.74259423
Coefficient of variation (CV)2.084314873
Kurtosis14.42512012
Mean10.43153053
Median Absolute Deviation (MAD)0
Skewness3.055738811
Sum1040952
Variance472.740404
MonotocityNot monotonic
2021-04-07T19:04:34.931525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
073704
73.9%
23728
 
0.7%
31683
 
0.7%
28667
 
0.7%
26661
 
0.7%
29656
 
0.7%
25650
 
0.7%
30647
 
0.6%
34638
 
0.6%
22635
 
0.6%
Other values (221)20120
 
20.2%
ValueCountFrequency (%)
073704
73.9%
113
 
< 0.1%
25
 
< 0.1%
39
 
< 0.1%
422
 
< 0.1%
542
 
< 0.1%
660
 
0.1%
772
 
0.1%
853
 
0.1%
960
 
0.1%
ValueCountFrequency (%)
2711
< 0.1%
2671
< 0.1%
2642
< 0.1%
2601
< 0.1%
2561
< 0.1%
2512
< 0.1%
2501
< 0.1%
2481
< 0.1%
2452
< 0.1%
2421
< 0.1%

tag_overview
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct13732
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06795935336
Minimum0
Maximum193
Zeros73704
Zeros (%)73.9%
Memory size779.7 KiB
2021-04-07T19:04:35.124104image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.03255813953
95-th percentile0.2583112583
Maximum193
Range193
Interquartile range (IQR)0.03255813953

Descriptive statistics

Standard deviation1.146020419
Coefficient of variation (CV)16.86332141
Kurtosis10218.38465
Mean0.06795935336
Median Absolute Deviation (MAD)0
Skewness84.08082887
Sum6781.595912
Variance1.3133628
MonotocityNot monotonic
2021-04-07T19:04:35.305329image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
073704
73.9%
0.166666666762
 
0.1%
0.12560
 
0.1%
0.333333333357
 
0.1%
0.142857142956
 
0.1%
0.256
 
0.1%
0.0909090909150
 
0.1%
0.2546
 
< 0.1%
0.111111111144
 
< 0.1%
0.144
 
< 0.1%
Other values (13722)25610
 
25.7%
ValueCountFrequency (%)
073704
73.9%
0.0013089005241
 
< 0.1%
0.0014124293791
 
< 0.1%
0.0019762845851
 
< 0.1%
0.0022123893811
 
< 0.1%
0.0025252525251
 
< 0.1%
0.0026315789471
 
< 0.1%
0.0036630036631
 
< 0.1%
0.0046948356811
 
< 0.1%
0.004796163071
 
< 0.1%
ValueCountFrequency (%)
1931
< 0.1%
1031
< 0.1%
831
< 0.1%
731
< 0.1%
721
< 0.1%
711
< 0.1%
671
< 0.1%
651
< 0.1%
621
< 0.1%
591
< 0.1%

Interactions

2021-04-07T19:02:52.453601image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:52.625932image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:52.779062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:52.940377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:53.090215image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:53.243158image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:53.386918image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:53.526005image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:53.662086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:53.803508image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:53.944226image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:54.084168image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:54.225932image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:54.367687image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:54.514705image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:54.650466image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:54.792700image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:54.943200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:55.091733image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:55.245015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:55.406530image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:55.547173image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:55.701038image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:55.840618image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:55.986972image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:56.134066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:56.277778image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:56.428273image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:56.570153image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:56.713800image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:56.851650image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:56.999452image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:57.137473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:57.278366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:57.422740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:57.558203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:57.708543image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:57.846335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:57.986757image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:58.142488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:58.286253image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:58.433934image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:58.573586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:58.713717image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:58.867091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:59.015028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:59.157499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:59.303346image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:59.456094image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:59.607442image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:59.765148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:02:59.907043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:00.062411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:00.222815image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:00.369156image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:00.517989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:00.661233image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:00.807253image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:00.960173image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:01.100866image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:01.241482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:01.388084image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:01.547590image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:01.704264image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:01.862656image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:02.011732image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:02.163803image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:02.309102image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:02.458584image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:02.603040image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:02.761774image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:02.922277image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:03.096464image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:03.244040image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:04.862755image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:05.017322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:05.171352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:05.316266image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:05.473497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:05.618852image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:05.772623image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:05.917337image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:06.067898image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:06.219526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:06.376549image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:06.535449image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:03:06.681758image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-04-07T19:04:07.395408image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:07.556195image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:07.714147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:07.874208image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:08.037717image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:08.191388image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:08.340334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:08.498936image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:08.651933image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:08.804950image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:08.959531image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:09.116189image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:09.275055image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:09.423696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:09.581361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:09.740806image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:09.900134image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:10.065302image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-07T19:04:10.220063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-07T19:04:35.615981image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-07T19:04:36.969299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-07T19:04:38.374933image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-07T19:04:39.692175image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-07T19:04:40.939462image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-07T19:04:10.884829image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-07T19:04:14.844166image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

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0093.8233586.65.00.9830005.00.676000201932014000000111000000010000001.0001100110076.000000000000.00.000000
1093.8233585.42.00.6000001.00.600000200002001000001111000000010000001.000000101000.000000000000.00.000000
2080.0000005.66.00.6000002.00.600000201406002001001111000100000000002.00000010000387.000000000000.00.000000
3094.0000006.36.00.6000003.00.600000201106003000001011000001000000001.00000000000433.000000000000.00.000000
4093.6997916.418.05.00670026.01.37150020011000100080501111000100000000003.00011100000385.0000000000067.00.173575
50100.0000007.36.01.0496675.00.678400200824014000001011000001000000002.00022000000185.000000000000.00.000000
6070.0000006.314.00.85020015.00.855900202028046001211001000000110000003.00044010000200.000000000000.00.000000
7097.0000006.915.01.7561007.00.861714200082025020100010000001010000003.00022000000377.000000000000.00.000000
8072.0000007.02.00.6640006.00.733333202002015000100011000000000010001.00011000000123.000000000000.00.000000
9093.8233586.849.03.12770037.02.00390020181000100060701001000000010000001.00011100000188.0000000000022.00.116402

Last rows

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99779096.0000007.94.01.2085001.01.400000201731010000100011000100000000001.010000000000.000000000000.00.000000
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99781087.0000006.511.00.72280011.00.6000002017280010000010001000000100000001.00000010000156.0000000000037.00.235669
99782093.0609246.825.05.40910024.00.77170020191000460100201011100000010001005.00022100000547.0000000000044.00.080292
997830113.0000006.048.04.09960045.01.50970020201000100080801001001001001000004.00011100000151.0000000000051.00.335526
99784092.0000005.02.01.0365001.00.600000201311001000001111000000010000001.000000100000.000000000000.00.000000
997850102.0000007.62.01.3755003.00.600000201711003000001001100000000010002.00000100000270.000000000000.00.000000
99786076.0000006.710.01.2314001.00.600000201773001001001011000101000000003.0001100000060.0000000000021.00.344262
997870112.0000003.98.00.8312502.00.600000201726002000011011000000000000001.00122000000274.000000000000.00.000000
997880113.0000005.86.01.3911676.00.733333201833015010001011000000000001003.000220000000.000000000000.00.000000

Duplicate rows

Most frequent

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11090.0000005.00.00.00.00.0200100000000001111000001000000001.0000010000020.000000000000.00.05
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1087.0071006.30.00.00.00.0201500000000001011000000000010001.000000000000.000000000000.00.02